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Deep Learning Methods for Speaker Separation in Reverberant Conditions
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Deep Learning Methods for Speaker Separation in Reverberant Conditions. |
| 개인저자 | Delfarah, Masood. |
| 단체저자명 | The Ohio State University. Computer Science and Engineering. |
| 발행사항 | [S.l.]: The Ohio State University., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 137 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-05B. Dissertation Abstract International |
| ISBN | 9781392673980 |
| 학위논문주기 | Thesis (Ph.D.)--The Ohio State University, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-05, Section: B.
Advisor: Wang, DeLiang. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | Speech separation refers to the problem of separating target speech from acoustic interference such as background noise, room reverberation and other speakers. An effective solution to this problem can improve speech intelligibility of human listeners and speech processing systems. Speaker separation is one kind of speech separation in which the interfering source is also human speech. This dissertation addresses the speaker separation problem in reverberant environments. The goal is to increase the speech intelligibility of hearing-impaired and normal-hearing listeners in those conditions.Speaker separation is traditionally approached using model-based methods such as Gaussian mixture models (GMMs) or hidden Markov models (HMMs). These methods are unable to generalize to challenging cases with unseen speakers or nonstationary noise. We employ supervised learning for the speaker separation problem. The idea is inspired from studies that introduced deep neural networks (DNNs) to speech-nonspeech separation. In this approach, training data is used to learn a mapping function from noisy speech features to an ideal time-frequency (T-F) mask.We start this study by investigating an extensive set of acoustic features extracted in adverse conditions. DNNs are used as the learning machine, and separation performance is evaluated using standard objective speech intelligibility metrics. Separation performance is systematically evaluated in both nonspeech and speech interference, a variety of SNRs, reverberation times, and direct-to-reverberant energy ratios. We construct feature combination sets using a sequential floating forward selection algorithm, and combined features outperform individual ones.Next, we address the problem of separating two-talker mixtures in reverberant conditions. We employ recurrent neural networks (RNNs) with bidirectional long short-term memory (BLSTM) to separate and dereverberate the target speech signal. We propose two-stage networks to effectively deal with both speaker separation and speech dereverberation. In the two-stage model, the first stage separates and dereverberates two-talker mixtures and the second stage further enhances the separated target signal. We have extensively evaluated the two-stage architecture, and our empirical results demonstrate large improvements over unprocessed mixtures and clear performance gain over single-stage networks.We then evaluate the performance of our proposed speaker separation systems using listening experiments in anechoic and reverberant conditions. Substantial intelligibility improvements were found for human listeners across a wide range of reverberation times and target-to-interfering ratios, and in some cases, hearing-impaired listeners with algorithm benefit match or surpass the performance of normal hearing people listening to unprocessed two-talker mixtures.We also address talker-independent speaker separation in which test speakers are unseen during training. Talker-independent solutions have mostly been limited to anechoic conditions. Here, we address reverberant talker-independent separation. We propose two-stage algorithms for the task by extending the deep CASA system that was recently proposed for talker-independent speaker separation. |
| 일반주제명 | Computer engineering. Computer science. Artificial intelligence. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
